Objectives:The global rise in diabetes highlights the need for innovative healthcare solutions. This scoping review is aimed at identifying and categorizing patient-reported facilitators and barriers influencing the use of telehealth consultations among people living with diabetes. Methods:A systematic scoping review was conducted following the Joanna Briggs Institute (JBI) methodology and PRISMA-ScR guidelines. Data Sources:Four databases-PubMed, Scopus, ISI Web of Science, and Google Scholar-were searched from inception to July 31, 2025. Eligibility Criteria:Included studies were original research or review articles published in English or Persian that examined patient-reported facilitators and barriers influencing the use of telehealth among people living with diabetes. Data Extraction and Synthesis:Two reviewers independently screened titles, abstracts, and full texts. Data were extracted using a predesigned form and synthesized thematically into facilitators and barriers. Results:Of 1673 identified records, 72 met inclusion criteria. Facilitators were grouped into four themes: process (e.g., time and cost savings), structural (e.g., system usability), communication (e.g., improved access), and attitudinal factors (e.g., awareness). Barriers included infrastructure limitations, digital literacy, privacy concerns, and resistance to change. Conclusions:Telehealth offers significant potential for diabetes management, but systemic and individual-level barriers must be addressed. Policymakers and healthcare providers should address patient-reported barriers while strengthening key facilitators to improve the adoption and sustainability of telehealth services in diabetes care.
Introduction:The metaverse, characterized by the convergence of virtual and physical realities, represents an emerging concept which offers immersive and interactive experiences with the potential to revolutionize medical care. This study aims to explore the current applications of this technology across various medical specialties. Method:A systematic search was conducted across five electronic databases including Web of Science, PubMed, Scopus, CINAHL, and IEEE, using a structured query in accordance with PRISMA guidelines. A qualitative analysis was conducted to synthesize findings across different categories such as medical domains, types of metaverse, implementation approaches, target groups, and clinical disciplines. Results:This review included 46 studies published between 2022 and 2024, with South Korea contributing the majority of publications (n = 12, 24%). Applications spanned diverse medical domains, notably medical education (n = 7, 15.22%), mental health (n = 6, 13.04%), and rehabilitation (n = 6, 13.04%). Among the included papers that focused on a single type of metaverse, virtual worlds appeared as the most prominent type (n = 23, 50.00%), followed by augmented reality (n = 8, 17.39%) and mirror worlds (n = 4, 8.70%). The majority of papers concentrated on designing and developing metaverse systems, primarily targeting patients (n = 24, 47.06%), medical students (n = 12, 23.53%), and healthcare providers (n = 8, 15.69%). These systems addressed multiple clinical phases, including treatment/intervention (n = 17, 36.95%), education (n = 10, 21.74%), and patient follow-up (n = 5, 10.87%). Conclusion:In conclusion, this review suggests that metaverse applications in healthcare are still in the early stages of development, focusing primarily on system design and feasibility rather than proven clinical outcomes. Although emerging technologies such as artificial intelligence and blockchain are emerging, evidence of clinical effectiveness remains limited. This review provides a descriptive roadmap of current trends and implementation frameworks to support prudent, evidence-based progress in understudied clinical areas.
Background:Telerehabilitation is an expanding domain of telehealth offering remote rehabilitation services. The quality of clinical guidelines, despite significant growth, is highly inconsistent, which may affect the quality of care and policy execution. We endeavored to systematically assess the quality of general telerehabilitation guidelines using the AGREE II instrument. Methods:A systematic search of six databases and grey literature identified 2575 records. After screening and eligibility assessment, seven guidelines were appraised independently by two reviewers using the AGREE II tool, covering six quality domains. Results:Only the American Physical Therapy Association's 2024 guideline was rated high quality across all domains. Three guidelines were rated medium quality and recommended with modifications. The remaining three were deemed low quality due to weak methodological rigor and limited stakeholder involvement. Conclusion:The quality of telerehabilitation guidelines differs significantly, and high-quality, evidence-based, and inclusive guidelines are essential for promoting secure, efficient, and telerehabilitation practices globally.
Background:Given that chronic diseases account for a considerable proportion of preventable deaths globally, the adoption of innovative technologies for disease management and prevention is crucial. Digital twins (DTs), representing one of the most advanced technological solutions, enable real-time simulation and monitoring of chronic disease progression, facilitating personalized treatment strategies and early intervention. This systematic review examines current research on DT applications in chronic disease management to evaluate their potential impact. Methods:A systematic search was conducted in four databases including PubMed, Scopus, Web of Science, and IEEE from inception to the date of the last search. The research question was formulated using PICO framework. Next, all articles were screened following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to select eligible articles based on inclusion criteria. The extracted information was analyzed to determine the main applications, domains, and employed technologies using quantitative and qualitative techniques. Results:Out of 298 citations, 20 studies met our inclusion criteria after duplicate removal and screening. Most studies (45%, n = 10) were published between 2023 and 2024, indicating an increasing trend in this area. Geographically, the United States contributed the most studies (25%, n = 5), followed by Switzerland (15%, n = 3). Our analysis revealed that primary applications of DT in chronic disease management included medical training and education (65%, n = 13), personalized medicine and patient care (45%, n = 9), and drug discovery and clinical trials (35%, n = 7). Target groups comprised clinicians (42.11%), patients (31.58%), and medical students (15.79%). Key enabling technologies in this subject were data analytics (65%), artificial intelligence and machine learning (60%), computational physiological modeling (30%), and IoT sensors (25%). Conclusions:Our findings demonstrate that DT technology has evolved from theoretical models to integrated clinical applications, with the potential to revolutionize healthcare through personalized medicine, continuous monitoring, and AI-driven decision support.
Health professional student-led clinics are commonly integrated into programmes of study to provide unique learning opportunities for students and provide improved access to care for underserved populations. Participant health outcomes from student-led clinics are infrequently reported and are hard to compare with outcomes from other intervention approaches. This study aims to examine change in participant health-relate-d quality of life following engagement in a telehealth mediated health coaching focussed student-led clinic in Australia. Pre-post data using the EQ-5D-5L instrument were collected from 369 participants in the student-led telehealth clinic between August 2020 and August 2024. Participant data were converted to a utility score relevant to an Australian population. The mean change in health utility score after participating in the clinic was 0.06 (95% CI 0.03-0.08, p < 0.001). Regression analyses showed that a significant positive change in mean utility score was observable in younger (18-35) participants (0.13, 95% CI 0.05-0.21, p = 0.001), females (0.06, 95% CI 0.02-0.09, p = 0.001) and participants with a lower baseline (< 0.6) utility score (0.14, 95% CI 0.07-0.20, p < 0.001). Significant change was observed across physical health, psychological and environmental concerns. As health coaching is known to be an important feature of preventative health strategies, this research is a promising indicator of how a student-led telehealth clinic may be an effective contributor to the health care ecosystem.
Examination and documentation (charting) of teeth are indispensable yet labor-intensive processes, especially in pediatric patients, where mixed dentition, anatomical variability, and limited cooperation during imaging present unique challenges. Despite their distinguished potential, existing AI models lack applicability to pediatric dentistry because they are mostly designed for adult dentition and use radiographic images. To fill this gap, we developed an AI-powered model for automated pediatric dental charting using real-time intraoral videos, utilizing a YOLO-based object detection framework. The model was trained to classify 44 tooth types, including primary and permanent dentitions. The dataset is composed of 112,538 frames extracted from 89 intraoral footages of children aged 6-12 years at diverse dental development stages. Of this dataset, 80% was allocated for training and 20% for testing. The model achieved a mAP@0.5 of 0.405, with a precision of 0.495 and a recall of 0.405 across all tooth classes. Notably, the model performed substantially better in detecting primary teeth, achieving a mAP@0.5 of 0.616, compared to 0.255 for permanent teeth, due to the latter's ongoing eruption and inconsistent appearance. Despite these limitations, this study is a major advancement toward automating pediatric dental charting and will pave the way for future developments in AI applications for pediatric dentistry, facilitating early caries detection for children in schools and in large-scale public health screening programs.
Background:Telerehabilitation (TR) has emerged as a promising approach to improving access to rehabilitation services, particularly in low- and middle-income countries such as Jordan. However, successful implementation largely depends on rehabilitation specialists' knowledge, attitudes, and perceived barriers. Aims:This study is aimed at exploring rehabilitation specialists' perspectives on TR in Jordan, focusing on their knowledge, attitudes, current use, and perceived implementation barriers. Methods:A cross-sectional study was conducted among rehabilitation specialists including physical therapist, occupational therapist, and speech and language therapist practicing in Jordan. Participants completed a validated 27-item Rehabilitation Specialists' Knowledge, Attitudes, and Barriers to TR Questionnaire. Descriptive statistics were used to summarize participants' demographic characteristics and their responses related to TR knowledge, attitudes, and perceived barriers. Results:A total of 300 rehabilitation specialists (mean age = 28.6 ± 6.1 years; 51% female) were included in the analysis, comprising equal numbers of physiotherapists, occupational therapists, and speech and language therapists. Overall, 81% of participants reported awareness of TR, and 57% indicated that TR was currently used in their workplace. Despite the generally favorable perceptions-59% considered TR socially somewhat acceptable and 75% believed it could save time, effort, or costs-actual engagement remained limited, with 35% reporting awareness without prior use and only 14% reporting regular use. Technological reliability and validity were endorsed by 78% and 72% of respondents, respectively. However, only 36% agreed that TR was comparable to face-to-face care. The most frequently reported barriers included limited public awareness (84%), technical issues such as poor internet connection (80%), provider willingness (74%), staff skill limitations including lack of training (71%), data privacy and security concerns (71%), location of healthcare institutions (69%), increased workload (69%), and lack of user-friendly software (61%). Early-career clinicians (< 5 years of experience), who constituted 67% of the sample, demonstrated greater openness toward TR compared with more experienced practitioners. Conclusion:Rehabilitation specialists in Jordan showed readiness to adopt TR. However, significant structural, educational, and cultural barriers appeared to hinder its widespread implementation.
Cervical cancer remains a major global health burden, particularly in underserved populations where late diagnoses contribute to high mortality rates. Accurate, early risk prediction is essential for improving outcomes and guiding preventive care. In this study, we introduce CERV-Score, a hybrid machine learning framework that advances prior approaches by combining structured clinical risk factors with recurrence-based genomic markers to generate continuous, probabilistic risk scores rather than traditional binary classifications. This enables nuanced patient stratification into low, moderate, and high-risk categories, providing clinicians with more actionable insights. Unlike previous models, CERV-Score integrates genomic recurrence analysis identifying genes consistently expressed across multiple RNA-seq samples to improve biological relevance and robustness. Additionally, we developed an interactive clinical-genomic decision support tool that delivers real-time, percentage-based risk predictions and includes a gene lookup function, bridging clinical practice and molecular exploration in a single platform. The hybrid CERV-Score model achieved high predictive performance (accuracy = 94.1%, F1 - score = 0.91, AUC = 0.94). Bootstrap resampling (1000 iterations) applied to the test predictions produced a 95% confidence interval for accuracy of 92.8%-95.4%, confirming the stability and robustness of the model ' s performance. These results highlight the contribution of probabilistic scoring, recurrence-driven genomic integration, and interactive visualization to enhance both accuracy and usability. By combining methodological innovation with practical clinical utility, CERV-Score represents a meaningful step beyond existing hybrid models, laying the groundwork for more interpretable, personalized, and deployable cervical cancer risk prediction systems.
Aims:This study is aimed at documenting the reflections of New Zealand healthcare professionals on the use of synchronous telehealth consultations. Methods:A qualitative narrative inquiry was conducted to explore the practice of telehealth in New Zealand. Purposive sampling was used to identify clinicians from multiple professions who used telehealth during the initial phase of the COVID-19 pandemic. Fifteen semistructured interviews were conducted between October 2020 and May 2021 with clinicians from primary and secondary care, including multiple professional backgrounds. Interview transcripts were analysed thematically. Results:Six themes were identified: (1) equitable access: There were concerns regarding equitable access to telehealth; (2) relationships and connections: This included connection with the whānau/family and their culture, between professions and as part of the wider health system; (3) information gathering and sharing: This included the visibility of the process as well as visibility of information regarding the client/patient; (4) adapting to change: There was significant variation between clinicians in transitioning to using telehealth; (5) professional boundaries: This included the prescribed boundaries such as the physical location of patients/clients, as well as unanticipated changes to personal, professional and organisational boundaries; (6) IT logistics: This included the potential technological drivers (enablers and disablers) within the process of incorporating telehealth. Conclusions:Telehealth was critical in healthcare provision during the COVID-19 pandemic and has continued to be used within healthcare delivery postpandemic. The themes identified provided insight into the importance of considering the provision of telehealth as a complex package and identifying contextual challenges as well as the enablers and potential benefits of using this modality.
Telemedicine has become an integral component of modern healthcare, particularly in contexts where direct patient-physician interaction is limited or unavailable. In such situations, access to objective physiological data, real-time patient localization, and automated alerting can significantly improve situational awareness and support timely clinical decision-making. This paper presents a real-time, location-aware patient monitoring system developed as a low-cost, fully functional demonstration platform to support telemedicine services and risk-based alerting. The proposed system combines wearable-based physiological data acquisition with a mobile application that collects heart rate measurements and geographic coordinates, which are transmitted to a server-side platform for real-time processing, visualization, and alert generation. Patient status is displayed through an interactive map with risk-level indicators and complemented by time-series charts that facilitate the interpretation of physiological trends over time. Incoming data are continuously evaluated using a rule-based risk assessment mechanism, enabling automated email alerts when predefined critical conditions are detected. Alert notifications include relevant physiological values together with direct links to the patient ' s geographic location, supporting rapid response in emergency or high-risk scenarios. The system is evaluated from a functional and architectural perspective, demonstrating its ability to support remote monitoring, contextual awareness, and decision support in telemedicine settings, including telephone-based consultations. While the platform does not aim to provide clinical validation or long-term medical assessment, it illustrates the practical benefits of integrating wearable data, real-time localization, and automated alerting within a unified telemedicine-oriented framework. In addition, the proposed architecture is designed to support future extensions based on data-driven methods, including machine learning-based risk prediction and preventive health analytics. However, such approaches are not part of the current implementation and are outlined as directions for future research. The main contribution of this work lies in the design and implementation of a low-cost, location-aware telemedicine monitoring system that combines real-time data acquisition, integrated visualization, and actionable alerting within a unified and deployable architecture.
Background:Somalia's healthcare system faces significant challenges due to limited infrastructure and physician density (2.5 per 10,000 population). Telemedicine is a promising solution, particularly given the country's mobile phone penetration rate of 54%. This study evaluated the implementation and impact of Baano Healthcare Technology's integrated telemedicine platform in Somalia, while also situating its utilization within the broader disparities in healthcare access, internet coverage, and socioeconomic context. Methods:A descriptive quantitative analysis of operational data from Baano Healthcare Technology telemedicine services was conducted between July and October 2024. These services were delivered through an integrated digital platform that linked video consultations, hospital bookings, and interactive voice response self-management services within a single telemedicine system. Data were collected through three primary channels: digital consultation, hospital bookings, and IVR self-management services. Statistical analysis was performed using R programming software Version 4.4.0, and descriptive statistics and frequency distributions of service utilization patterns were calculated. Demographic data for digital users included age, sex, and residence, whereas IVR records lacked user-level metadata, which limited stratification. Results:This study analyzed 610 users of video consultation and hospital booking services, along with 157,660 interactive voice response system interactions. The analysis revealed that 63.44% of users were aged 1-30 years, with a balanced sex distribution (50.82% male and 49.18% female). Hospital bookings constituted 73.61% of the services, whereas online consultations accounted for 26.39%. The Banadir region accounted for 80.49% of all users in the study. Dental services were the most requested specialty (42.98%), reflecting the scarcity of licensed dentists outside Mogadishu and the platform's role in facilitating access to rare specialties in the region. The IVR system was substantially used for chronic condition management (47%), with diabetes management being the most frequently accessed topic (23%). Conclusion:The implementation of integrated telemedicine services in Somalia demonstrates promising potential for expanding healthcare access, particularly in urban areas. However, its reach remains constrained by geographic and digital divides, with rural areas facing compounded barriers of poverty, provider scarcity, and low internet use. The platform's success in urban areas provides a model for expansion, although addressing infrastructure limitations and regulatory frameworks remains important.
Background The potential of well‐being chatbots as supportive tools in mental healthcare is increasingly recognised. Nevertheless, user acceptance remains low, highlighting the need to understand the factors influencing adoption and engagement. Objective: The purpose of this study is to identify the factors influencing user acceptance and engagement with chatbots and to generate insights that can inform the design and implementation of more effective chatbot interventions for mental well‐being. Methods: Following the PRISMA 2020 guidelines, an integrative review was conducted in June 2024. Literature searches were carried out in Web of Science, Scopus, PubMed, IEEE Xplore and ScienceDirect to identify peer‐reviewed articles published between January 2010 and May 2024. Thematic analysis was employed using an inductive approach. Results: From a total of 1232 papers identified, 20 studies met the inclusion criteria. The findings developed three themes, which involve technological factor, user factor and environmental factor. Different subtopics within the technological aspects have different effects on user behaviour. Technological limitations and excessive anthropomorphism have emerged as key barriers to user–chatbot interaction; empathy, interactivity, user‐centred design, ease of use, personalisation, usability and stability were found to promote user engagement. From the user perspective, barriers included lack of motivation, low trust, privacy concerns and effort expectations, while facilitators encompassed enjoyment, positive attitudes, learning opportunities and emotion. Environmental factors could influence user adoption behaviour, for instance, advertising and social influences. Conclusions: Multiple interdisciplinary factors have been found to influence user engagement with chatbots for mental well‐being. This will contribute to refining extant theories and fostering interdisciplinary collaboration. Moreover, this study provides a valuable source of instruction for designers and developers of health chatbots.
Background Mobile and app‐based digital health interventions have rapidly emerged as transformative tools for breast cancer prevention, diagnosis, treatment adherence, and survivorship support. However, the expanding evidence base remains fragmented across multiple systematic reviews and meta‐analyses of varying quality. Objective This umbrella review was aimed at synthesizing and critically appraising the evidence from systematic reviews and meta‐analyses evaluating mobile and app‐based digital health interventions across the breast cancer care continuum, including prevention/early detection support, treatment adherence, symptom self‐management, psychosocial outcomes, communication, and survivorship. Methods A comprehensive search of PubMed, Scopus, and Web of Science was conducted from January 2000 to October 2025, following PRISMA 2020 and Joanna Briggs Institute (JBI) umbrella review guidelines. Eligible studies included systematic reviews or meta‐analyses examining mobile or app‐based interventions across prevention, early detection support, treatment, survivorship, adherence, symptom management, psychosocial support, and care coordination. Methodological quality was assessed using AMSTAR‐2, and overlap was evaluated through the corrected covered area (CCA). Both qualitative syntheses and quantitative meta‐analytic comparisons were performed, with subgroup analyses by app type (AI‐based vs. rule‐based) and implementation setting (clinical vs. community). Results Twelve systematic reviews and meta‐analyses published between 2016 and 2025 met the inclusion criteria. Most reviews demonstrated high methodological quality (67% rated “high” on AMSTAR‐2) and reported consistent short‐term benefits in patient engagement, symptom control, treatment adherence, and quality of life. Pooled analyses of comparable outcome measures within domains showed similar effectiveness across app types (AI − based pooled effect = 0.42; rule − based pooled effect = 0.36) and settings (clinical pooled effect = 0.38; community pooled effect = 0.33) with negligible heterogeneity ( I 2 = 0 % ). Visual inspection of funnel and bubble plots did not suggest marked asymmetry; however, interpretation should be cautious given that synthesis was conducted at the review level. Recent reviews indicated a shift toward AI‐driven, interoperable, and validated platforms with improved methodological rigor under PRISMA 2020 standards. Conclusions Evidence is strongest for short‐term improvements in engagement, adherence, symptom management, and psychosocial outcomes. Evidence for validated diagnostic performance and long‐term clinical endpoints remains limited and heterogeneous.
Offering scalable solutions to solve inequities in access, quality, and efficiency, the fast development of mobile health (mHealth) systems has transformed healthcare delivery all around. By combining the technology acceptance model (TAM), the theory of planned behavior (TPB), and the information system success model (ISSM), this study examines the elements propelling the adoption of mHealth services in Iran. A thorough model for this study based on literary review is created using influential elements. Testing the model and assumptions is a sample of 400 possible mHealth service users; the data is examined using structural equation modeling techniques. Examination of hypothesis tests showed that perceived usefulness had a very strong positive effect on adoption intention (beta = 0.65, t = 13.07), while perceived ease of use had a moderate but significant negative direct impact (beta = -0.42, t = 4.60). On the other hand, attitude did not have any predictive power for intention or mediating effects from ease of use or usefulness (t < 1.10). While also contributing favorably-though to a lesser extent-to perceived usefulness (beta = 0.31, t = 3.92) and directly to adoption intention (beta = 0.17, t = 3.74), subjective norms emerged as a ubiquitous influence, most significantly on attitude (beta = 0.37, t = 6.37) and ease of use (beta = 0.62, t = 13.49). Among the quality dimensions, information quality (beta = 0.26, t = 2.86) and service quality (beta = 0.45, t = 5.80) greatly enhanced perceived usefulness; system quality remained nonsignificant (t = 1.85). Finally, efficacy beliefs displayed conflicting results: Self-efficacy had modest yet significant positive impacts on intention (beta = 0.05, t = 2.04) and attitude (beta = 0.12, t = 2.27), without influencing ease of use; response efficacy gave moderate positive effects on ease of use (beta = 0.36, t = 3.76). These results refute fundamental TAM hypotheses-especially the unfavorable role of ease of use and the nonsignificance of attitude-and highlight the overriding importance of social influence and content quality in resource-restricted mHealth environments. The results offer practical guidance for legislators and developers striving to create resilient, user-centered mHealth solutions that carefully balance simplicity, usability, and infrastructural realities.
BackgroundThe Indonesian government has established a blueprint for health system digitalization aimed at improving health coverage. Despite the benefits of telemedicine services, its utilization remains low, and the factors associated with nonuse of telemedicine in Indonesia are not well understood.ObjectiveThis study aimed to assess the prevalence of telemedicine use and to identify factors contributing to its nonuse among patients with hypertension and/or diabetes, particularly considering that these patients require long-term medication management and monitoring.MethodsThis national cross-sectional study utilized data from the Indonesia Health Survey conducted in 2023, reflecting the postpandemic demographical conditions across 38 provinces in Indonesia. Telemedicine utilization and sociodemographic information were assessed based on a self-reported questionnaire. Logistic regression was performed to identify sociodemographic factors associated with nonuse of telemedicine. Odds ratios (ORs) with 95% confidence intervals (CIs) were reported.ResultsThis study involved 63,012 patients with diabetes and/or hypertension. Most of them were women (65.1%), married (78.3%), aged 55-64 years (30.9%). Nearly all the respondents (98.0%) had not used telemedicine. Factors associated with nonuse of telemedicine included being unmarried (OR = 1.40; 95%CI = 1.11-1.77), older than 34 years (OR = 3.83; 95%CI = 1.90-7.73), having an educational background below the university level, farmer/fisherman and helper/laborer/driver, and living outside the islands of Java and Bali. Respondents with hypertension alone (OR = 1.67, 95%CI = 1.32-2.11) were more likely to report nonuse of telemedicine compared with those with both diabetes and hypertension.ConclusionsThe usage of telemedicine among patients with hypertension and/or diabetes in Indonesia is low. Personalized approaches that consider patient-specific factors and integrate telemedicine more frequently into the healthcare system are essential to enhance telemedicine adoption among patients with hypertension and/or diabetes in Indonesia.
Introduction: Parkinson ' s disease (PD) places a substantial burden on caregivers, affecting their quality of life and potentially compromising patient care. Mobile health (mHealth) interventions may help reduce these challenges. This study was aimed at evaluating the effect of a mobile application on perceived stress and self-efficacy among caregivers of older adults with PD. Method: This randomized controlled clinical trial was conducted with 80 caregivers recruited from the Neurology Clinic of Qaim Hospital, Iran. Participants in the intervention group received access to a PD management mobile application along with face-to-face training, while the control group received only face-to-face training at the clinic. Both groups completed the Cohen Perceived Stress Inventory and the Caregiver Self-Efficacy Scale at baseline, immediately after the intervention, and 1 month later. Results: Immediately after the intervention, the intervention group demonstrated significantly lower perceived stress compared to the control group (p = 0.018). However, this difference was not sustained at the 1-month follow-up (p = 0.115). Within-group analyses showed no significant change in stress levels over time (p > 0.05). Self-efficacy scores improved in the intervention group, particularly in the domains of "gathering information about treatment" (p = 0.031) and "completing household tasks" (p = 0.041). Conclusion: The mobile application improved caregivers ' self-efficacy and temporarily reduced perceived stress, suggesting its potential as a supportive tool for individuals caring for older adults with PD. Integrating mHealth solutions may enhance caregiver well-being and contribute to better caregiving outcomes.
Background:Older adults are prone to multimorbidity and polypharmacy, which often lead to adverse outcomes such as increased hospital admissions and treatment nonadherence. Smartphone and internet use among older adults in India is rising, but its potential for addressing healthcare needs like multimorbidity management and drug adherence remains underexplored. The "Know Your Meds (KYM)" Creda Health mobile application (app) on the Google Play Store serves as a digital health assistant, offering features such as medication information, drug interaction insights, and pill reminders to improve health outcomes. This randomized controlled trial is aimed at assessing the effectiveness of the AI-based mobile app KYM in improving clinical outcomes, medication adherence, and patient satisfaction among older Indian adults. Methodology:In this randomized controlled trial, 360 participants with multimorbidity (aged > 60 years) were randomly allocated into intervention (n = 182) and control (n = 175) groups with the intervention group using the KYM app for 12 weeks, whereas the control group received standard conventional healthcare. Results:Although clinical outcomes like change in blood pressure, HbA1c, and lipid levels did not show a significant difference between the two groups, there was a significant difference in medication adherence at 12-week follow-up. However, no significant change was observed in patient satisfaction. Conclusion:The study highlights the potential of mobile health apps in promoting adherence, though further research is required to evaluate their impact on clinical outcomes with more tailored interventions.
Purpose:We developed an innovative telerehabilitation system using a 3D camera with motion sensors that provided real-time feedback. This study is aimed at evaluating its efficacy in improving balance, gait, and mobility, as well as its feasibility in patients with idiopathic Parkinson's disease (PD). Materials and Methods:Participants with idiopathic PD self-selected into either a telerehabilitation (tele) group or a hospital-based rehabilitation (hospital) group. The tele group received two initial sessions of hospital-based rehabilitation sessions, followed by 14 telerehabilitation sessions using the innovative system. The hospital group received 16 sessions of hospital-based rehabilitation. Outcome measures included Berg Balance Scale (BBS) score, Chula Parkinson Mobility Scale (Chula PMS) score, gait speed, and step length. The feasibility of the telerehabilitation system was also assessed. Results:Forty-six participants were recruited (tele group: n = 23; hospital group: n = 23). Both groups showed statistically significant improvements in the BBS scores (tele: post-pre = 3.50, p < 0.001; hospital: post-pre = 4.35, p < 0.001), with no statistically significant difference between the groups (mean difference: -0.85, p = 0.454). Chula PMS score also improved significantly in both groups (tele: post-pre = 3.45, p < 0.001; hospital: post-pre = 5.70, p < 0.001) without a statistically significant difference between the groups (mean difference: -2.25, p = 0.086). The attendance rate exceeded 90% in both groups. Conclusions:The motion sensor telerehabilitation significantly improved balance and mobility in PD patients with no statistically significant differences between the two groups. Feasibility was high. However, the BBS improvements did not reach the minimal clinically important difference, indicating the need for further investigation. Trial Registration:Thai Clinical Trials Registry identifier: TCTR 20220924001.
Background:Children living in underserved and rural areas experience significant barriers to healthcare access due to geographic isolation, health workforce shortages, and systemic inequities. Digital and remote health interventions such as telehealth, telemental health (TMH), and mobile health (mHealth) offer promising strategies to improve pediatric health outcomes in these contexts. However, the extent of their effectiveness remains insufficiently examined through high-quality evidence. Methods:A systematic review was conducted in accordance with PRISMA 2020 guidelines and structured using the PROPS framework. Five databases (PubMed, Scopus, Web of Science, Embase, and Cochrane Library) were searched for randomized controlled trials (RCTs) published until May 2025. Eligible studies targeted children (0-17 years) in underserved or rural settings and evaluated digital or remote interventions versus standard care. Data were extracted on study design, population, intervention modality, outcomes, and implementation characteristics. Risk of bias was assessed using the Cochrane RoB 2.0 tool. Results:Eleven RCTs were included, covering interventions for obesity, asthma, ADHD, diabetes, oral health, and neonatal care. Telehealth interventions improved behavioral and biometric outcomes (e.g., BMI z-score, and adherence), particularly in the United States. TMH showed high fidelity and effectiveness for ADHD management. mHealth interventions in low- and middle-income countries enhanced referral rates, service coverage, and caregiver engagement. Most studies were rated low risk of bias, though few incorporated economic or equity analyses. Conclusions:Digital health interventions are effective and feasible for improving pediatric outcomes in underserved settings. Future research should emphasize long-term impact, cost-effectiveness, and equitable access to ensure sustainable and inclusive digital healthcare delivery.
